Event based behavior prediction, classification, and service adjustment

ABSTRACT

Various systems, mediums, and methods for providing services may involve data engines configured to generate scores associated with one or more entities and then to classify the entities based on the scores. The data engine may collect data and based on the collected data may generate a first behavior model in a first time span, and a second and third behavior models in a second time span. The data engine may generate a first score based on the first behavior model, a second score based on the second behavior model, and a third score based on the third behavior model. The data engine may generate a final score based on the first, second, and third scores. The data engine can classify the entity based on the final score. The data engine can then automatically adjust one of the services provided to the entity based on the final score.

TECHNICAL FIELD

Embodiments disclosed herein are related to systems and methods forpredicting behavior of an entity that provides or receives services. Thebehavior prediction can be based on events occurring in multiple timespans. The entity can be classified based on the prediction and theservices can be adjusted based on the classification.

BACKGROUND

Various types of data engines involve a number of inputs and one or morecorresponding outputs. In particular, the inputs may be provided by oneor more sources. Also, a data engine may determine which inputs toutilize and which inputs to discard, possibly based on the particularsources that provide the inputs. Under various circumstances, the dataengine may utilize such selected inputs to determine one or morecorresponding scores.

Multiple types of scores may be computed. In some examples, a score mayreflect a given performance of an entity in a predetermined period oftime. In some examples, the score may reflect working hours of an entityin a week, duration of physical exercises of a person in a day, and/orthe like. Also, the score may reflect completing assigned tasks of anentity in predetermined deadlines, payment of bills by the deadlines(e.g., on-time payment), and/or the like. In some examples, the scoremay be based on various inputs, analyses, computations, and/orevaluations implemented by the data engine on qualifying events receivedin one or more predetermined periods of time. Yet, in some respects, thecomputed score may be utilized to determine potential risks possiblyassociated with the particular entity. In some examples, the computedscore may be inaccurate or erroneous, possibly leading to unreliablemeasures associated with the particular entity.

Various types of data engines may involve generating or gathering datarelated to different features of an object and/or a behavior and then,based on the data, generating one or more scores related to the featuresof the object and/or the behavior and then based on the scores ofdifferent features may generate a final score for the object and/or thebehavior. A classification module may then classify the object and/orthe behavior based on the final score.

As demonstrated above, there is much need for technological advancementsin various aspects of data engines and related technologies toaccurately determine scores generated by the data engines.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a block diagram of an exemplary system, according to someembodiments.

FIG. 2A is a block diagram of an exemplary data engine, according tosome embodiments.

FIG. 2B is a block diagram of an exemplary data engine, according tosome embodiments.

FIG. 3A is a block diagram of an exemplary system, according to someembodiments.

FIG. 3B illustrates an exemplary data engine, according to someembodiments.

FIG. 4A illustrates an exemplary timeline with events on the timelineaccording to some embodiments.

FIG. 4B illustrates an exemplary timeline with events on the timelineaccording to some embodiments.

FIG. 5 is a block diagram of an exemplary system, according to someembodiments.

FIG. 6 illustrates an exemplary method, according to some embodiments.

Embodiments of the present disclosure and their advantages may beunderstood by referring to the detailed description provided herein. Itshould be appreciated that reference numerals may be used to illustratevarious elements and/or features provided in the figures. Further, thefigures may illustrate various examples for purposes of illustration andexplanation related to the embodiments of the present disclosure and notfor purposes of any limitation.

DETAILED DESCRIPTION

In the following description specific details are set forth describingcertain embodiments. It will be apparent, however, to one skilled in theart that the disclosed embodiments may be practiced without some or allof these specific details. The specific embodiments presented are meantto be illustrative, but not limiting. One skilled in the art may realizeother material that, although not specifically described herein, iswithin the scope and spirit of this disclosure.

In some embodiments, a system provides one or more services to an entityand the system monitors a performance of the entity with respect to oneor more events, goals, or outcomes associated with the providedservices. Alternatively, the entity provides services and a systemmonitors the performance of an entity with respect to services providedby the entity. An entity may include a person, a company, an enterprise,a firm, a factory, a device, a bridge, a street, among other things. Insome examples, the entity is a factory and utility services are providedto the factory and a score may be associated with the utility usage ofthe factory. In some examples, the entity is a device such as arefrigerator and electric power is provided to the refrigerator and ascore may be associated with the power usage of the refrigerator. Insome examples, the entity is a street or a bridge that provides trafficservices and a score may be associated with the congestion of the streetor bridge. Example embodiments herein describe data engines and relatedtechnologies to generate accurate scores for entities that accuratelyclassify the entity and consequently adjust the services.

Consistent with some embodiments, there is provided a system having anon-transitory memory for storing at one or more databases. The systemprovides one or more services to an entity. The system includes one ormore hardware processors that are configured to execute instructions.The instructions when executed as one or modules by the one or morehardware processors and to cause the system to perform operationsdescribed herein.

In some embodiments, the system collects data corresponding to theperformance of the entity. The data is collected by a data collectionand processing module of a data engine of the system from the databasesof the system.

In some embodiments, a first behavior model is generated. A modelingmodule of the data engine generates the first behavior model based atleast on the collected data. The first behavior model is associated withthe performance of the entity in a first time span. Also, an occurrenceof a predetermined event is determined by the data engine (e.g., whetherthe event happens) in the first time span. The first time span mayoptionally correspond to a future time span following the current time.

In some embodiments, a second behavior model and a third behavior modelare also generated. The second behavior model and the third behaviormodel are generated by the modeling module and based at least on thecollected data. The second behavior model and the third behavior modelare associated with the performance of the entity in a second time span.The second time span is different from the first time span and mayoptionally correspond to a future time span following the current time.The second behavior model may further be based on the occurrence of thepredetermined event in the first time span and the third behavior modelis further based on the non-occurrence of the predetermined event in thefirst time span.

In some embodiments, based at least on the first behavior model, a firstscore is generated by the score generating module of the data enginecorresponding to the occurrence of the predetermined event. The scoregenerating module also generates a second score based at least on thesecond behavior model and a third score based at least on the thirdbehavior model. The second and third score correspond to the predefinedoutcome (e.g., likelihood of occurrence of the outcome) in the secondtime span. A final score is generated by the score generating module ofthe data engine, based at least on the first score, second score, andthe third score. The final score corresponds to achieving a predefinedoutcome related to the performance of the entity in a final time span.The final time span may optionally correspond to a future time spanafter the current time and may have a duration greater or equal to aminimum of the first time span and second time span.

In some embodiments, a classifier module of the data engine thenclassifies the entity into two or more groups. The classification isbased at least on the final score. At least one of the one or moreservices provided by the system to the entity is adjusted based on thefinal score, the classification, or both the final score andclassification.

Consequently, as described, embodiments described herein may allowproviding services to an entity, e.g., a person, a factory, a company, astore, a bridge, a street, and/or the like based on scores. Theembodiments also describe achieving one or more outcomes by the entity.The embodiments described herein may then determine (e.g., classify),based on the scores, which one of the goals or outcomes ispossible/achievable by the entity. The embodiments described herein mayfurther determine, based on the scores and classification, whichcorrective or stopping measures should be applied to/by the entity.

In various circumstances, the example embodiments described herein mayresolve various challenges with regards to monitoring and automaticallyadjusting services provided by an entity or alternatively, automaticallyadjusting services provided to an entity. As such, the exampleembodiments described herein may resolve problems that did not existbefore the availability of the computer networks, particularlygenerating scores related to future outcomes and automatically adjustingservices provided to or by an entity. Notably, the modules may beimplemented with hardware, software, or possibly with aspects of bothhardware and software.

FIG. 1 is a simplified block diagram of an exemplary system 100,according to some embodiments. The system 100 may provide one or moreservices to an entity. As shown, the system 100 includes a data engine102. The data engine 102 includes a data collection and processingmodule 104, possibly also referred to as an initial data collection andprocessing module 104. Further, the data engine 102 includes a modelingmodule 108. Yet further, the data engine 102 includes an incrementaldata module 106. In addition, the data engine 102 includes a scoregenerating module 110 and a classifier module 112. In some embodiments,the data collection and processing module 104, the incremental datamodule 106, the modeling module 108, the score generating module 110,and the classifier module 112 may be implemented using hardwarecomponents, such as a processor, an application specific integratedcircuit (ASIC), a programmable system-on-chip (SOC), afield-programmable gate array (FPGA), and/or programmable logic devices(PLDs), among other possibilities. As shown, the data collection andprocessing module 104, the incremental data module 106, the modelingmodule 108, the score generating module 110, and the classifier module112 may be coupled to a bus, network, or other connection 114. Yet, itshould be noted that any two or more of the data collection andprocessing module 104, the modeling module 108, the incremental datamodule 106, the score generating module 110, and the classifier module112 may be combined within a single hardware component, such as theprogrammable SOC.

The data collection and processing module 104 may be configured tocollect data 115. The data collection and processing module 104 may alsobe configured to process the collected data 115. As shown, the datacollection and processing module 104 may output the processed data 116.Further, the data engine 102 may supply the modeling module 108 and thescore generating module 110 with the processed data 116. In someexamples, the data collection and processing module 104 may collect data115 during an initial data gathering phase. The collected data 115 maybe associated with various aspects of one or more entities. Inparticular, the collected data 115 may include details of the one ormore entities related to performances of the entities, possiblyincluding an indication of performance associated with the one or moreentities. The indication of performance may include a range ofperformance, possibly spanning from high performance to mildperformance, to low performance, and/or no performance. Further, theindication of performance may include a time span of performancepossibly spanning from an hour, to a day, to a week, to a month, to ayear, to several years, and/or the like. The collected data 115 mayinclude details of individuals, companies, objects, events, and/or thelike identified as the one or more entities related to the performances.As noted above, the system 100 may be used for a number of differentsystems and thus the details of the entities may depend on the entityand may include physical, structural, financial, and/or othercharacteristics of the entities.

As noted, the data collection and processing module 104 may beconfigured to gather the collected data 115. In some embodiments, thecollected data 115 may be processed based on the data collection andprocessing module 104 and/or the configurations of the data collectionand processing module 104, as described further herein. In someinstances, the data collection and processing module 104 may detect textdata from the collected data 115 and convert the text data to numericdata. As such, the text describing the characteristics of the entitiesmay be converted from text data to numeric data. In some instances, thenumeric data may be part of the collected data 115 transferred to thedata collection and processing module 104.

In some embodiments, the data collection and processing module 104 maynormalize the collected data 115. In some examples, the data collectionand processing module 104 may normalize the collected data 115 such thatthe data related to the one or more characteristics described above maybe compared. For instance, data associated with the usage of an entitymay be compared with the data associated with the capacity of theentity. In some instances, the values representing different scores ofthe entity may be normalized to the same or similar scale, such as ascale from zero to one hundred. With various such values measured on thesame scale, the values may be compared and/or analyzed accordingly. Insome examples, when the score is related to an outcome, the normalizedscore may be interpreted as a percent probability of the outcome tooccur.

In some instances, the data collection and processing module 104 maynormalize the collected data 115 by organizing, arranging, ordering,and/or structuring the collected data 115. Further, the data collectionand processing module 104 may normalize the collected data 115 byremoving redundant data sets from the collected data 115. In someinstances, the data collection and processing module 104 may normalizethe collected data 115, where the collected data 115 may correspond tothe one or more of the characteristics of the entities described above,such as the usage, the capacity, the assets, the liabilities, thestatements, and/or the like associated with the entities. As such, thecharacteristics may be structured into a number of tables to minimizethe size of the collected data 115, without losing information or data.In some instances, the collected data 115 may be normalized based on theconfiguration of data collection and processing module 104, theconfigurations of the modeling module 108, the configurations of thescore generating module 110, and/or the configurations of the classifiermodule 112.

In some embodiments, the data collection and processing module 104 maydetect and/or remove one or more outliers from the collected data 115.As noted, respective values representing the characteristics of theentity (e.g., the usage, the capacity, the assets, the liabilities, thescore, and/or the like of the entity) may be normalized to the same orsimilar scale, such as a scale from zero to one hundred. Further, thedata collection and processing module 104 may detect and/or removeoutliers from the collected data 115 based on the deviations of theoutliers from mean values associated with the collected data 115. Inparticular, the data collection and processing module 104 may detectoutliers by retrieving a number of samples from the collected data 115.The data collection and processing module 104 may determine a mean valuethat corresponds to the number of samples. The data collection andprocessing module 104 may also determine a standard deviation associatedwith the samples. In some examples, the data collection and processingmodule 104 may detect and remove one or more outliers from a given databased at least on that outliers deviating from the mean data value bymore than some multiple, such as 2.5, 3.0, and/or the like, of thestandard deviation.

In some embodiments, the data collection and processing module 104 mayprocess and configure the data for the modeling module 108. As shown,the data collection and processing module 104 may transfer the processeddata 116 to the modeling module 108, possibly over the bus, network, orother connections 114 to generated behavior models 117. In someinstances, the processed data 116 may also be transferred to the scoregenerating module 110 from the modeling module 108. In some instances,the one or more scores may be obtained based on transferring thegenerated behavior models 117 and the processed data 116 to the scoregenerating module 110. In some instances, the one or more results 118may be obtained based on transferring the generated scores 119 to theclassifier module 112. In some examples, the classifier module 112 mayoutput the one or more results 118, possibly due to classifying theentity based on at least the scores obtained from the score generatingmodule 110 and possibly other characteristics of the entity describedabove. In some examples, the scores may be related to achieving apredefined outcome corresponding to a performance of the entity and theclassifier module 112 may classify the entity based on achieving thepredefined outcome. In some examples, if the results 118 includeclassifying the entity as not achieving the predefined output, then thesystem may stop service provided to the entity.

As described above, the system 100 may provide one or more services tothe entity and/or the system 100 may monitor the performance of theentity. In some examples, based on the scores generated by the scoregenerating module 110, or based on the classification output of theclassifier module 112, based on both the scores and the classification,the system 100 may adjust the services provided to the entity. In someexamples, if the scores are high the system 100 may improve the servicesto the entity, provide more services for the entity, and/or the like.Alternatively, if the scores are low the system 100 may reduce theservices to the entity, stop one or more services of the entity, demandone or more services and/or a compensation from the entity, and/or thelike.

In some embodiments, the incremental data module 106 may furtherintroduce data, possibly based on receiving extra information includingupdated data. In some examples, the incremental data module 106 maycollect additional data related to one or more given entities, possiblytaking similar form to the collected data 115 including text andnumerical data. In particular, the incremental data module 106 mayprocess the additional data and transfer the processed data to themodeling module 108 to update the behavior models and to the scoregenerating module 110 to update the generated scores 119. Further, thescore generating module 110 may output the updated generated scores tothe classifier module 112 to generate updated results, possibly takingsimilar form to the one or more results 118. As such, the incrementaldata module 106 may further enhance the modeling module 108 and thescore generating module 110.

In some embodiments, the data collection and processing module 104 maytransfer additional data and/or a stream of data to the modeling module108 and the score generating module 110. In some examples, the datacollection and processing module 104 may collect one or more streams ofdata, possibly where the data is associated with one or more additionalentities. Further, the data collection and processing module 104 maytransfer the data to the modeling module 108 and then to the scoregenerating module 110 to obtain scores, possibly including a score thatrepresents the one or more additional entities. In some instances, thescore may take the form of a numerical expression. In particular, thescore may further represent an assessment of trustworthiness,dependability, credibility, and/or risk, among other features and/orcharacteristics related to a performance associated with the one or moreentities. In some instances, the score may be viewed as a search score,a travel score, a health score, a credit score, an approval score,and/or the like.

FIG. 2A is a block diagram of an exemplary data engine 200, according tosome embodiments. The data engine 200 includes a modeling module 202, ascore generating module 210 and a classifier module 222. In someembodiments, the data engine 200 is consistent with the data engine 102of FIG. 1 or the data engine 350 of FIG. 3B. Yet further, the modelingmodule 202 includes a first time span behavior model generator 204 andsecond time span behavior model generators 206 and 208. In someexamples, the first time span behavior model generator 204 receives data216, consistent with processed data 116 of FIG. 1, and based on data216, generates the first behavior model. The first behavior model ispart of the behavior models 217 generated by the modeling module 202. Insome examples, the second time span behavior model generators 206 and208 receive data 216, consistent with processed data 116 of FIG. 1, andbased on data 216, generates the second and third behavior models. Thesecond and third behavior model are part of the behavior models 217generated by the modeling module 202. In some examples, the behaviormodels 217 are consistent with the behavior models 117 of FIG. 1.

In some embodiments, the second time span behavior model generator 206generates the second behavior model based on an occurrence of event-A(e.g., given event-A occurs) in the first time span, and the second timespan behavior model generator 208 generates the third behavior modelbased on occurrence of the event-B (e.g., given event-B occurs) in thefirst time span. In some examples, the score generating module 210generates the scores 219 that are consistent with the scores 119 ofFIG. 1. The score generating module 210 uses the behavior models 217 andgenerates the first, the second, the third, and the final score. In someexamples, the classifier module 222 may output the one or moreactions/outcomes 218, possibly due to classifying the entity based atleast on the scores 219 obtained from the score generating module 210and possibly other characteristics of the entity described above.

In some examples, event-A is the predetermined event, and event-B is acomplement (e.g., an opposite) of event-A such that the first scorecorresponds to the likelihood of an occurrence of the event-A in thefirst time span, e.g., the probability event-A occurring in the firsttime span. Also, the second score corresponds to an occurrence of thepredefined outcome occurring in the second time span given event-Aoccurring in the first time span. Additionally, the third scorecorresponds to an occurrence of the predefined outcome occurring in thesecond time span given event-A not occurring in the first time span.

FIG. 2B is a block diagram of an exemplary data engine 250, according tosome embodiments. The data engine 250 includes a modeling module 252, ascore generating module 260 and a classifier module 262. In someembodiments, the data engine 250 may be consistent with the data engine102 of FIG. 1 and/or data engine 350 of FIG. 3B. Yet further, themodeling module 252 includes a long-term behavior model generator 254and short-term behavior model generators 256 and 258. In some examples,the long-term behavior model generator 254 receives data 266, consistentwith processed data 116 of FIG. 1, and based on data 266, generates thefirst behavior model. The first behavior model is part of the behaviormodels 267 generated by the modeling module 252. The behavior models 267are consistent with the behavior models 117 of FIG. 1. In some examples,the short-term behavior model generators 256 and 258 receive data 266,consistent with processed data 116 of FIG. 1, and based on data 266,generates the second and third behavior models. The second and thirdbehavior model are part of the behavior models 267 generated by themodeling module 252. The behavior models 267 are consistent with thebehavior models 117 of FIG. 1.

In some embodiments, the short-term behavior model generator 256generates the second behavior model based on an occurrence of theevent-A (e.g., given event-A occurs) in the long-term, and theshort-term behavior model generators 258 generates the third behaviormodel based on occurrence of the event-B (e.g., given event-B occurs) inthe long-term. In some examples, the score generating module 260generates the scores 269 that are consistent with the scores 119 ofFIG. 1. The score generating module 260 uses the behavior models 267 andgenerates the first, the second, the third, and the final score. In someexamples, the classifier module 262 may output the one or moreactions/outcomes 268, possibly due to classifying the entity based atleast on the scores 269 obtained from the score generating module 260and possibly other characteristics of the entity described above.

In some examples, event-A is the predetermined event, and event-B is acomplement (e.g., an opposite) of event-A such that the first scorecorresponds to the likelihood of an occurrence of the event-A in thelong-term, e.g., the probability event-A occurring in the long-term.Also, the second score corresponds to an occurrence of the predefinedoutcome occurring in the short-term given event-A occurring in thelong-term. Additionally, the third score corresponds to an occurrence ofthe predefined outcome occurring in the short-term given event-A notoccurring in the long-term.

FIG. 3A is a simplified block diagram of an exemplary system 300,according to some embodiments. As shown, the system 300 includes one ormore processors 302 that may execute the data engine 350. In someembodiments, the date engine 350 is consistent with the data engine 102of FIG. 1. The system 300 includes a network communication interface310. Yet further, the system 300 includes a communication link (e.g.,bus) 312 between the processors 302 and the network communicationinterface 310. In addition, the system 300 includes anothercommunication link (e.g., bus) 314 between the processors and thenon-transitory memory 304. In some embodiments, the processors 302, thenetwork communication interface 310, and the memory 304 may beimplemented using hardware components, such as an application specificintegrated circuit (ASIC), a programmable system-on-chip (SOC), afield-programmable gate array (FPGA), and/or programmable logic devices(PLDs), among other possibilities. Yet, it should be noted that any twoor more of the processors 302, the network communication interface 310,and the memory 304 may be combined within a single hardware component,such as the programmable SOC.

FIG. 3B is a simplified block diagram of an exemplary data engine,according to some embodiments. In some embodiments, the data engine 350may be consistent with the data engine 320 of FIG. 3A and execute on theprocessors 302 and/or the data engine 102 of FIG. 1. As described, thedata engine 350 includes modules that may execute by the processors 302.The data engine 350 includes a data collection and processing module 334and an incremental data module 332. Yet further, the data engine 350includes a modeling module 326 and a score generating module 324. Inaddition, the data engine 350 includes a classifier module 328 and asecurity module 322. The modules of the data engine communicate witheach other through the communication links 336, 338, 342, 344, 346, 348,and 330. The communication links are controlled by the processors 302and communication may be performed through pipelines or memory buffers.In some embodiments, the data engine 350 may include hardware modulesseparate from the processors 302 that may communicate with each otherand with the processors 302 through the communication links 336, 338,342, 344, 346, 348, and 330.

In some embodiments, the data engine 350 may be consistent with the dataengine 320 of FIG. 3A, and the data collection and processing module 334may communicate through communication link 314 with the databases 308and may collect (e.g., read) data from the databases 308. In someembodiments, the incremental data module 332 receives updated data,possibly from network communication interface 310 and throughcommunication link 312. In some examples, the incremental data module332 receives updated data through communication link 314 from the memory304.

As noted, the data engine 350 and its modules may be implemented usinghardware components. In some examples, the data collection andprocessing module 334, the incremental data module 332, the modelingmodule 326, the score generating module 324, the classifier module 328,and/or the security module 322 may be implemented using hardwarecomponents inside the system, e.g., system 300 of FIG. 3A, and thecommunication links 336, 338, 342, 344, 346, 348, and 330 may beimplemented using communication buses such that the modules communicatethrough the communication link 330 with the processors 302 and possiblyto the memory 304. Additionally the data engine 350 may be implementedusing an application specific integrated circuit (ASIC), a programmablesystem-on-chip (SOC), a field-programmable gate array (FPGA), and/orprogrammable logic devices (PLDs), among other possibilities. Yet, itshould be noted that any two or more of the module of the data engine350 may be combined within a single hardware component, such as theprogrammable SOC.

Referring back to FIG. 1, in some embodiments, the modeling module 108of the data engine 102 generates a first behavior model associated withthe performance of the entity in a first time span. The modeling modulegenerates the first behavior model based on the processed data 116. Insome examples, the first behavior model predicts the performance of theentity in the first time span. In some examples, the modeling module 108is consistent with the modeling module 326 of the data engine 350 inFIG. 3B. As shown in FIG. 1, the first behavior model is included in thebehavior models 117 that are transferred through connections 114 to thescore generating module 110. Also, in some examples, the behavior models117 are consistent with the behavior models 217 and 267 in FIG. 2A andFIG. 2B. Additionally, in some examples, the modeling module 108 isconsistent with the modeling modules 202 and 252 in FIG. 2A and FIG. 2Band the first behavior module is generated by the first time spanbehavior model generator 204 or the long-term behavior model generator254.

In some embodiments, the modeling module 108 of the data engine 102generates a second behavior model associated with the performance of theentity in a second time span. The modeling module generates the secondbehavior model based on the processed data 116 as well as based on theoccurrence of the predetermined event in the first time span. In someexamples, the second behavior model predicts the performance of theentity in the second time span given that the predetermined event occursin the first time span. As shown in FIG. 1, the second behavior model isincluded in the behavior models 117 that are transferred throughconnections 114 to the score generating module 110. In some examples,the modeling module that generates the second behavior model isconsistent with the modeling module 326 of the data engine 350 in FIG.Also, in some examples, the behavior models 117 are consistent with thebehavior models 217 and 267 in FIG. 2A and FIG. 2B. Additionally, insome examples, the modeling module 108 is consistent with the modelingmodules 202 and 252 in FIG. 2A and FIG. 2B and the second behaviormodule is generated by the second time span behavior model generator 206or the short-term behavior model generator 256.

In some embodiments, the modeling module 108 of the data engine 102generates a third behavior model associated with the performance of theentity in a second time span. The modeling module generates the thirdbehavior model based on the processed data 116 as well as based onnon-occurrence of the predetermined event in the first time span. Insome examples, the second behavior model may predict the performance ofthe entity in the second time span given the predetermined event doesnot occur in the first time span. As shown in FIG. 1, the third behaviormodel is included in the behavior models 117 that are transferredthrough connections 114 to the score generating module 110. In someexamples, the modeling module that generates the third behavior modelmay be consistent with the modeling module 326 of the data engine 350 inFIG. 3B. Additionally, in some examples, the modeling module 108 isconsistent with the modeling modules 202 and 252 in FIG. 2A and FIG. 2Band the third behavior module is generated by the second time spanbehavior model generator 208 or the short-term behavior model generator258.

In some embodiments, the second and third behavior models instead ofbeing based on an occurrence and a non-occurrence of the same event(i.e., completely correlated events) are based on separate events wherethe separate events might be partially correlated or may be independentof each other.

In some embodiments, the score generating module 110 of the data engine102 generates a first, a second, and a third score. The first score isgenerated based at least on the first behavior model and correspondingto an occurrence of the predetermined event. In some examples, the firstscore is a probability of the predetermined event happening in the firsttime span. The second score is generated based at least on the secondbehavior model, and the third score is generated based at least on thethird behavior model. The second and third scores correspond to anoccurrence of the predefined outcome. In some examples, the second scoreand the third score are probabilities of the predefined outcomehappening in the second time span. As shown in FIG. 1, the generatedscores may be included in the scores 119 that are transferred throughconnections 114 to the classifier module 112. In some examples, thescore generating module 110 may be consistent with the score generatingmodule 324 of the data engine 350 in FIG. 3B. Also, in some examples,the score generating module 110 is consistent with the score generatingmodule 210 and 260 in FIG. 2A and FIG. 2B.

In some embodiments, the score generating module 110 of the data engine102 generates a final score. The final score corresponds to achievingthe predefined outcome corresponding to the performance of the entity ina final time span. The final time span is greater than or equal to aminimum of a first time span and a second time span. In some examples,the first time span is 3 months, the second time span is one month, andthe final time span is also one month.

In some embodiments, the classifier module 112 of the data engine 102classifies the entity into two or more groups. In some examples, theclassifier module 112 classifies the outcome into two groups, a firstgroup of entities that are likely to achieve the predefined outcome, anda second group of entities that are not likely to achieve the predefinedoutcome. In some examples, the classifier module 112 may be consistentwith the classifier module 328 of the data engine 350 in FIG. 3B. Also,in some examples, the classifier module 112 is consistent with theclassifier module 222 and 262 in FIG. 2A and FIG. 2B.

In some embodiments, the data engine 102 adjusts the services providedto the entity. The adjustment is performed based on the final score, theclassification, or both. In some examples, the adjustment is reducing orstopping one or more services when the entity is not likely to achievethe predefined outcome (e.g., performance). In some examples, theadjustment is improving the services when the entity in likely toachieve the predefined outcome. Additionally, the adjustment may includewarning the entity when the entity is not likely to achieve thepredefined outcome. In some examples, the data engine 102 is consistentwith the data engine 350 in FIG. 3B. Also, in some examples, the dataengine 102 is consistent with the data engine 200 and 250 in FIG. 2A andFIG. 2B.

In some embodiments, the classifier module 112 is used to classify theoutcome or performance of an individual, a company, an object, and/orthe like. Classification is performed based on features associated withthe individual, the company, the object, and/or the like. In someexamples, generated values (e.g., scores) are assigned to the featuresand the classifier module 112 uses the scores of the features togenerate a classification decision. In some examples, the outcome is agenerated (e.g., calculated) value such as a performance score based onthe scores of the features. In some examples, the outcome is a Yes/Novalue.

As described in the examples below, the data engine determines (e.g.,selects) a predetermined event in the first time span and a predefinedoutcome in the second time span. In some examples, the predeterminedevent is correlated to the predefined outcome. In some examples, thepredetermined event is uncorrelated to the predefined outcome. In someexamples, the data engine determines (e.g., selects) a predeterminedevent in the long-term and a predefined outcome in the short-term.

In some examples, the predefined outcome is the determination of whethera city street or bridge is uncongested. A feature for classifying thetraffic on the street or bridge as congested or uncongested may bedefined as the number of vehicles on the street or bridge. Thepredefined spans of time are set as one day for the first time span andone hour for the second time span. The traffic is measured per hour andthe value assigned to each span is simply the number of vehiclestraveling on the street or the bridge during the respective time span.The predetermined event is defined as a football game happening the nextday. The score generating module generates a final score correspondingto an outcome, such as, the congestion on the street or bridge in thehour before the game. In some examples, the score is normalized betweenzero and one hundred and then is interpreted as the percent probabilityof the outcome to occur. In yet some other examples, and based on thescore, the system controlling the bridge or the street changes thenumber of traffic lanes in the opposite direction to remedy the problemif the score is low, i.e., congestion is likely to happen. In someexamples, the modeling module may additionally use other factors whengenerating the models, for example, gasoline price, toll of thestreet/bridge, maximum speed on the street/bridge, the weather forecastof the next day, and/or the like.

In some embodiments, the predefined outcome is the determination ofwhether the employees of a company make business trips. A databaseincludes the travel data related to employees of a company. In someexamples, the databases may include data related to travellingactivities of one or more employees in periods equal to a first timespan where the first time span may be one year. Also, the database mayinclude data related to travelling activities of the same employees inperiods equal to a second time where the second time span may be 3months. In some examples, the predetermined event is defined that thecompany shares gain ten percent value in the next year. The scoregenerating module generates a final score corresponding to a predefinedoutcome, for example, a predetermined number of business trips to occurin a final time span which is at least 3 months. In some examples, thescore is normalized between zero and one hundred and then is interpretedas the percent probability of the outcome to occur. In some examples,the modeling module may additionally use other factors when generatingthe models, for example, gasoline price, weather conditions, economicdownturn, and/or the like.

In some embodiments, the predefined outcome is the determination ofwhether an individual makes an exercise such as walking. A databaseincludes the daily exercises related to the individual. For example, thedatabase may include data related to the number of steps taken by anindividual in a first time spans of one month while the database mayalso include data related to the number of steps taken by the sameindividual in a second time span of one day. In some examples, thepredetermined event is defined as ten percent weight gain by theindividual in the next month. The score generating module generates afinal score corresponding to an outcome, for example, a predeterminednumber of steps to be taken by the same individual in a final time spanwhich is at least a day, e.g., the next day. In some examples, the scoreis normalized between zero and one hundred and then is interpreted asthe percent probability of the outcome to occur. In some examples, themodeling module may additionally use other factors when generating themodels, for example, weather forecast, employment conditions, and/or thelike.

In some embodiments, the predefined outcome is the determination ofwhether an individual makes online purchases. A database includes theonline purchase activities performed by an individual. For example, thedatabase may include data related to a number specific websites visitedby the individual and online purchase activities occurring in a firsttime span of one year while the database may also include data relatedto the specific websites visited by the same individuals and onlinepurchase activities occurred in a second time span of one month. In someexamples, the predetermined event is defined as the individual receivesa ten percent raise in the next year. The score generating modulegenerates a final score corresponding to an outcome, for example, theoutcome of a predetermined number of online purchase activitiesperformed by the same individual in a final time span which is at leasta month, i.e., the next month. In some examples, the score is normalizedbetween zero and one hundred and then is interpreted as the percentprobability of the number of online purchase activities to occur in thefinal time span. In some examples, the modeling module may additionallyuse other factors when generating the models, for example, whether theindividual is married, the number of children, the age of children,employment conditions, and/or the like.

In some embodiments, the predefined outcome is the determination of theapproval rating of a politician or a TV show. A database includes thedata related to approval ratings, e.g., approval ratings of a TV show orapproval ratings of a politician. For example, the database may includedata related to the approval ratings in a first time span of one yearwhile the database may include data related to the approval ratings in asecond time span of one month. In some examples, the predetermined eventselected is that a misuse of funds be reported for the TV station or thepolitician in the next year. The score generating module generates afinal score corresponding to an outcome, for example, the outcome ofhaving at least 70% approval rating in a final time span which is atleast one month, e.g., in the next month. In some examples, the score isnormalized between zero and one hundred and then is interpreted as thepercent probability of having a specific approval rating in the finaltime span. In some examples, the modeling module may additionally useother factors when generating the models, for example, whether thepolitician or the TV show director is married, and/or the like.

In some embodiments, the predefined outcome is the determination that anindividual makes a payment towards a credit balance. A database includesthe online payment activities performed by an individual. For example,the database may include data related to online payments by theindividual in a first time spans of three months while the database mayinclude data related to online payments by the same individuals in asecond time span of one month. In some examples, the predetermined eventis that the individual going delinquent in the next 3 months. The scoregenerating module generates a final score corresponding to an outcome,for example, the outcome that the individual makes an online payment ina final time span which is at least a month, i.e., in the next month. Insome examples, the score is normalized between zero and one hundred andthen is interpreted as the percent probability of the online payments inthe final time span. In some examples, the modeling module mayadditionally use other factors when generating the models, for example,the percentage of months in the past 3, 6, and/or 12 months that theindividual made payments, the number of months in the past 12 months theindividual's account was past due (was delinquent), maximum balance inpast 12 months, number of days in collections in the past 3 and/or 6months, maximum balance in the past 3 months, number of days since alast payment, a last payment amount, and an average monthly purchaseamount in past 6 months.

In some embodiments, the features used by the classifier module may havefuzzy values. In some examples, the traffic in a street instead ofhaving a value may be categorized as heavy, mild, or occasional and theclassifier module may use these fuzzy values to reach a classification.

In some embodiments, the modeling module implements Bayesian methodologysuch that a probability is assigned to a predefined outcome based on theprior data. In some examples, when new data becomes available, the newdata becomes the next prior data.

In some embodiments, Gradient Boosting techniques are used to generatethe first, second, and/or third behavior models. In some examples, themodels are built in a stage-wise fashion and optimization is performedby an arbitrary differentiable loss function.

In some examples, at least the first score is on a probability scalebetween zero and one and the final score is then calculated using therelation: second score*first score+third score*(1−first score).

In some embodiments, the collected data in different spans of time maybe correlated. Additionally, generating the second behavior model andthe third behavior model, by the modeling module, e.g., the modelingmodule 108 of FIG. 1, includes determining relationships in thecollected data in different spans of time. The spans of time that areequal to the first time span and the second time span are defined andthe relationships (e.g., correlation) of the collected data in the firsttime spans, in the second time spans, and between the first and secondtime spans are determined. In some embodiments, independentrelationships in the collected data in different spans of time aredetermined.

In some embodiments, the first score has a first accuracy, the secondscore has a second accuracy, and the third score has a third accuracy.The final score has a final accuracy better than the first accuracy, thesecond accuracy, and the third accuracy.

In some embodiments, the operations further include determining thefirst accuracy by the score generating module and based on the firstbehavior model, determining the second accuracy by the score generatingmodule and based on the second behavior model, and determining the thirdaccuracy by the score generating module and based on the third behaviormodel. The operations further include determining the final accuracy bythe score generating module and based on the first, second, and thirdscores. In some embodiments, the final accuracy is a prediction accuracyand thus it is a final prediction accuracy.

FIG. 4A illustrates an exemplary timeline with events on the timelineaccording to some embodiments. The diagram 400 shows the timeline 425that may be related to the system 100 of FIG. 1. The timeline 425 showsa number of qualifying events 405 distributed on the timeline. Thetimeline 425 also shows a first time span 410. In some examples, thetimeline 425 shows four qualifying events 405 in the first time span410. As described above, the qualifying event 405 may correspond to atraffic measurement per hour, an online payment, and/or the like.

FIG. 4B illustrates an exemplary timeline with events on the timeline.The diagram 450 shows the timeline 425 of diagram 400 with the samequalifying events distributed on the timeline that may be related to thesystem 100 of FIG. 1. The diagram 400 also shows the second time spans452, 454, 456, and 458. In some examples, as shown in FIGS. 4A and 4B,the first time span is 3 times the second time span and the first timespan 410 may include the second time spans 452, 454, and 456. Also, insome examples, the second time span 452 includes two qualifying events405, the second time span 454 does not include any qualifying event 405,the second time span 456 includes two qualifying events 405, and thesecond time span 458 includes one qualifying event 405. In someexamples, the second time span has a duration of one month and the firsttime span has a duration equal to three months or three times theduration of the first time span.

FIG. 5 is a block diagram of an exemplary network system 500, accordingto some embodiments. The network system 500, possibly referred to as thenetwork system for providing services, may be configured to transferdata over one or more communication networks 508. In particular, thenetwork system 500 may include the system/server 501. In some examples,system/server 501 may be consistent with system 300 of FIG. 3A. Thesystem/server 501 may be configured to perform operations of a serviceprovider, such as PayPal, Inc. of San Jose, Calif., USA. Further, thenetwork system 500 may also include user device 505 and/or the userdevice 506 operated by their respective users. In practice, thesystem/server 501 and the user devices 505 and/or 506 may be configuredto communicate over the one or more communication networks 508.

In some embodiments, the network system 500 may include the server 501.The server 501 includes the non-transitory memory 504. The server 501 ofthe network system 500 has one or more hardware processors 502 coupledto the non-transitory memory 504 and configured to read the instructionsfrom the non-transitory memory 504 to cause the network system 500 toperform the operations as described further below.

The network system 500 may operate with more or less than the computingdevices shown in FIG. 5, where each device may be configured tocommunicate over one or more communication networks 508, possibly totransfer data accordingly. The one or more communication networks 508may also include a packet-switched network configured to provide digitalnetworking communications, possibly to exchange data of various forms,content, type, and/or structure. The one or more communication networks508 may include a data network such as a private network, a local areanetwork, a wide area network, and/or the like. In some examples, the oneor more communication networks 508 may include a communications networksuch as a telecommunications network and/or a cellular network with oneor more base stations, among other possible networks.

The data/data packets 522 and/or 524 may be transferable usingcommunication protocols such as packet layer protocols, packet ensembleprotocols, and/or network layer protocols. In some examples, thedata/data packets 522 and/or 524 may be transferable using transmissioncontrol protocols and/or internet protocols (TCP/IP). In variousembodiments, each of the data/data packets 522 and 524 may be assembledor disassembled into larger or smaller packets of varying sizes. Assuch, data/data packets 522 and/or 524 may be transferable over the oneor more networks 508 and to various locations in the network system 500.

In some embodiments, the server 501 may take a variety of forms. Theserver 501 may be an enterprise server, possibly configured with one ormore operating systems to facilitate the scalability of the networksystem 500. In some examples, the server 501 may configured with aUnix-based operating system to integrate with a growing number of otherservers, user devices 505 and/or 506, and one or more networks 508 overthe network system 500.

In some embodiments, the system/server 501 may include multiplecomponents, such as a hardware processor 502, a non-transitory memory504, a non-transitory data storage 516, and/or a communication interfacecomponent 510, among other possible components, any of which may becommunicatively linked via a system bus, network, or other connectionmechanism 520. The hardware processor 502 may be implemented using amulti-purpose processor, a microprocessor, a special purpose processor,a digital signal processor (DSP) and/or other types of processingcomponents. In some examples, the processor 502 may include anapplication specific integrated circuit (ASIC), a programmablesystem-on-chip (SOC), and/or a field-programmable gate array (FPGA) toprocess, read, and/or write data for providing services to numerousentities. In particular, the processor 502 may include a variable-bit(e.g., 64-bit) processor architecture specifically configured tofacilitate the scalability of the increasing number of entities. Assuch, the one or more processors 502 may execute varying instructionssets (e.g., simplified and complex instructions sets) with fewer cyclesper instruction than other conventional general-purpose processors toimprove the performance of the server 501 for purposes of massscalability and/or accommodation of growth.

The non-transitory memory component 504 and/or the data storage 516 mayinclude one or more volatile, non-volatile, and/or replaceable datastorage components, such as a magnetic, optical, and/or flash storagethat may be integrated in whole or in part with the hardware processor502. Further, the memory component 504 may include a number ofinstructions and/or instruction sets. The processor 502 may be coupledto the memory component 504 and configured to read the instructions tocause the server 501 to perform operations, such as those described inthis disclosure, illustrated by the accompanying figures, and/orotherwise contemplated herein. Notably, the data storage 516 or memory504 may be configured to store numerous user data, possibly includingdata that may be accessed often by the user devices 505 and/or 506. Insome examples, the user data may include user ID and access codes orauthentication tokens of a user.

The communication interface component 510 may take a variety of formsand may be configured to allow the server 501 to communicate with one ormore devices, such as the user devices 505 and/or 506. In some examples,the communication interface component 510 may include a transceiver 519that enables the server 501 to communicate with the user devices 505and/or 506 via the one or more communication networks 508. Further, thecommunication interface component 510 may include a wired interface,such as an Ethernet interface, to communicate with the user devices 505and/or 506. Yet further, the communication interface component 510 mayinclude a wireless interface, such as a cellular interface, a GlobalSystem for Mobile Communications (GSM) interface, a Code DivisionMultiple Access (CDMA) interface, and/or a Time Division Multiple Access(TDMA) interface, among other possibilities. In addition, thecommunication interface 510 may include a wireless local area networkinterface such as a WI-FI interface configured to communicate with anumber of different protocols. As such, the communication interface 510may include a wireless interface configured to transfer data over shortdistances utilizing short-wavelength radio waves in approximately the2.4 to 2.485 GHz range. In some instances, the communication interface510 may send/receive data or data packets 522 and/or 524 to/from userdevices 505 and/or 506.

The user devices 505 and 506 may also be configured to perform a varietyof operations such as those described in this disclosure, illustrated bythe accompanying figures, and/or otherwise contemplated herein. Notably,the data storage 536/546 of the user devices 505 and 506 may beconfigured to store numerous user data, possibly including data that maybe accessed often by the user devices 505 and 506 such as geographicdata, movement data, exercise data, among other types of data associatedwith the user. In some examples, the user devices 505 and 506 may beconfigured to authenticate a user of the user devices 505 and 506 basedon data stored, e.g., a security token, in the user devices.Alternatively, the server 501 may authenticate a user based on receivingthe security token from the user devices 505 and 506.

In some embodiments, the user devices 505 and 506 may include or beimplemented as a user device system, a personal computer (PC) such as alaptop device, a tablet computer device, a wearable computer device, ahead-mountable display (HMD) device, a smart watch device, and/or othertypes of computing devices configured to transfer data. The user devices505 and 506 may include various components, including, for example,input/output (I/O) interfaces 530 and 540, communication interfaces 532and 542 that may include transceivers 533 and 543, hardware processors534 and 544, and non-transitory data storages 536 and 546, respectively,all of which may be communicatively linked with each other via a systembus, network, or other connection mechanisms 538 and 548, respectively.

The I/O interfaces 530 and 540 may be configured to receive inputs fromand provide outputs to respective users of the user devices 505 and 506.In some examples, the I/O interface 530 may include a display thatprovides a graphical user interface (GUI) configured to receive an inputfrom a user. Thus, the I/O interfaces 530 and 540 may include displaysconfigured to receive inputs and/or other input hardware with tangiblesurfaces, such as touchscreens with touch sensitive sensors and/orproximity sensors. The I/O interfaces 530 and 540 may also include amicrophone configured to receive voice commands, a computer mouse, akeyboard, and/or other hardware to facilitate input mechanisms, possiblyto authenticate a user. In addition, I/O interfaces 530 and 540 mayinclude output hardware such as one or more sound speakers, other audiooutput mechanisms, haptic feedback systems, and/or other hardwarecomponents.

In some embodiments, communication interfaces 532 and 542 may include ortake a variety of forms. In some examples, communication interfaces 532and 542 may be configured to allow user devices 505 and 506,respectively, to communicate with one or more devices according to anumber of protocols described and/or contemplated herein. For instance,communication interfaces 532 and 542 may be configured to allow userdevices 505 and 506, respectively, to communicate with the server 501via the one or more communication networks 508. The hardware processors534 and 544 may include one or more multi-purpose processors,microprocessors, special purpose processors, digital signal processors(DSP), application specific integrated circuits (ASIC), programmablesystem-on-chips (SOC), field-programmable gate arrays (FPGA), and/orother types of processing components.

The non-transitory data storages 536 and 546 may include one or morevolatile or non-volatile data storages, removable or non-removable datastorages, and/or a combination of such data storages that may beintegrated in whole or in part with the hardware processors 534 and 544,respectively. Further, data storages 536 and 546 may includenon-transitory memories that store instructions and/or instructionssets. Yet further, the processors 534 and 544 may be coupled to the datastorages 536 and 546, respectively, and configured to read theinstructions from the non-transitory memories to cause the user devices505 and 506 to perform operations, respectively, such as those describedin this disclosure, illustrated by the accompanying figures, and/orotherwise contemplated herein.

In some embodiments, the communication interface 510 is coupled to theone or more processors 502. In some examples, a security moduleconsistent with the security module 322 of FIG. 3B receives anauthentication request from a client device, e.g., client device 505/506of a requester through the network interface 510 and a network 508. Inresponse to authenticating the requester, a security key is transmittedthrough the network interface 510 and the network to the client device505/506 of the requester. The request to generate the final score and toclassify the entity is received through the network interface 510 andthe network. The data engine transmits encrypted data, corresponding tothe final score of the entity and the entity classification through thenetwork interface 510 and the network to the requester. The clientdevice 505/506 of the requester uses the received security key toautomatically display the encrypted data.

In some embodiments, the classification of the entity includes assigningthe entity to a success group if the final score is greater than a firstpredetermined threshold, assigning the entity to a failure group if thefinal score is less than a second predetermined threshold, and assigningthe entity to a neutral group if the final score is between the firstand second thresholds. The classification of the entity also includesadjusting the services provided to the entity. Adjusting may includestopping at least one of services if the entity is assigned to thefailure group. In some examples, when the entity is assigned to thefailure group, the adjustment may include sending notifications, e.g.,sending one or more warning notifications.

In some embodiments, updated data of the entity is received by anincremental data module 332 of the data engine 350. The received updateddata is temporarily stored in a memory 304 separate from the database308. The updated data is compared with existing data corresponding tothe entity in the database 308. A relationship between the receivedupdated data and the data in the database 308 is detected. Based on thedetected relationships, it is determined whether to store the updateddata in the database 308, or to discard the updated data.

In some embodiments, updated data for the entity is received by anincremental data module 332 of the data engine 350. The received updateddata is constantly monitored. The first score, second score, and thethird score is automatically updated based at least on the receivedupdated data. The final score is also automatically updated based atleast on the updated first score, second score, and the third score. Theentity is automatically reclassified based at least on the updated finalscore.

FIG. 6 illustrates an exemplary method 600, according to someembodiments. Notably, one or more steps of the method 600 describedherein may be omitted, performed in a different sequence, and/orcombined with other methods for various types of applicationscontemplated herein. The method 600 may be performed by any of the dataengines 102, 200, 250, 320, and/or 350. The method 600 can be used as amethod of providing services to an entity as well as a method ofproviding services by an entity. In some embodiments, one or moreprocesses, e.g., steps of the method 600, is implemented by instructions(e.g., software instructions) stored on the non-transitory memory, e.g.,non-transitory memory 304 of FIG. 3A.

As shown in FIG. 6, at step 602, the method 600 may include collectingdata corresponding to performance of the entity. In some examples,collecting data may be performed by the data collection and processingmodule 104 of data engine 102 and/or the data collection and processingmodule 334 of data engine 350. In some examples and as discussed abovethe collected data might be the traffic data of the bridge or street,the travel data of the employees of a company, daily exercise data of anindividual, online purchasing data of an individual, approval ratingdata of a TV show or a politician, data related an individual's theonline payment, and/or the like.

In some embodiments, the collected data is processed. In some examples,referring to FIG. 1, the data collection and processing module 104processes the collected data 115 including normalizing the data,removing outliers, and/or the like and providing the processed data 116.

At step 604, the method 600 may include generating a first behaviormodel associated with performance of an entity in a first time span anddetermining an event in the first time span. The first behavior modelcan be generated based at least on the collected data. In some examples,determining (e.g., selecting) the predetermined event may be performedby the data engine 102, the date engine 200, and/or the data engine 250.In some examples, generating the first behavior model can be performedby the modeling module 108, the first time span behavior model generator204 of the modeling module 202, and/or the long-term behavior modelgenerator 254 of model generator 252. In some examples, the first timespan may correspond to the next three months. In some embodiments, andas discussed above the predetermined event might be a football gameoccurring in a next day, a company's shares gain ten percent value in anext year, a ten percent weight gain by an individual in a next month,an individual receiving a ten percent raise in a next year, a reportedmisuse of funds be a TV station or a politician in the next year, anindividual going delinquent in a next 3 months, and/or the like.

At step 606, the method 600 may include generating the second behaviormodel and the third behavior model associated with the performance ofthe entity in the second time span and determining a predefined outcomein the second time span. In some examples, determining (e.g., selecting)the predefined outcome may be performed by the data engine 102, the dateengine 200, and/or the data engine 250. In some examples, generating thesecond behavior model can be performed by the modeling module 108, thesecond time span behavior model generator 206 of the modeling module202, and/or the short-term behavior model generator 256 of modelgenerator 252. Likewise, generating the third behavior model can beperformed by the modeling module 108, modeling module 326, the secondtime span behavior model generator 208 of the modeling module 202,and/or the short-term behavior model generator 258 of model generator252.

In some examples, the second behavior model can be generated using thecollected data and based on the occurrence of a determined event (e.g.,event-A) in the first time span. Also, the third behavior model can begenerated using the collected data and based on the occurrence ofanother determined event (e.g., event-B) in the first time span. In someexamples, event-B is correlated to event-A. In some examples, event-B iscompletely correlated to event-A such that it is a non-occurrence ofevent-A.

In some examples, the first time span is a year and the second time spanis a month, the first time span is a month and the second time span is aday, the first time span is a day and the second time span is an hour,and/or the like. In some examples, the second time span has a largerduration than the first time span.

In some embodiments, and as discussed above the predefined outcome mayoccur in a final time span and might be a determination of whether acity street or bridge is uncongested, a determination of whether theemployees of a company make a predetermined number of business trips, adetermination of whether an individual takes a predetermined number ofsteps, a determination of whether an individual makes online purchases,a determination of the approval rating of a politician or a TV show, adetermination of whether an individual making a payment in the nextmonth, and/or the like.

At step 608, the method 600 may include generating the first score, thesecond score, and the third score and then generating the final score.In some examples, generating the scores may be performed by the scoregenerating module 110 of data engine 102, the score generating module324 data engine 350, the score generating module 210 data engine 200,and/or the score generating module 260 of data engine 250.

In some examples, and as discussed above, the first score corresponds topredicting the occurrence of the predetermined event in a first timespan, such as predicting the occurrence of the football game in a nextday, predicting the occurrence of company's shares gaining ten percentin a next year, predicting the occurrence of an individual receiving aten percent raise in a next year, predicting the occurrence of areported misuse of funds by a TV station or a politician in a next year,predicting the occurrence of an individual going delinquent in a next 3months, and/or the like.

In some examples, the second score corresponds to the predefined outcomesuch as predicting the occurrence of the predefined outcome in a secondtime span. In some examples, and as discussed above, the second scorecorresponds to predicting the occurrence of the predefined outcome giventhe predetermined event has occurred such as predicting a street orbridge being uncongested in the next hour given a football game occursin the next day, predicting a number of business trips for a company inthe next 3 months given the company shares value raise ten percent inthe next year, predicting a number of steps taken by an individual inthe next day given a ten percent weight gain by the individual in a nextmonth, predicting a number of online purchases made by an individual inthe next month given the individual receiving a ten percent raise in anext year, predicting the approval rating of a politician or a TV showin the next month given a reported misuse of funds by the TV station orthe politician in the next year, predicting making a payment by anentity in the next month given the entity going delinquent in the next 3months, and/or the like.

In some examples, the third score corresponds to the predefined outcomesuch as predicting the occurrence of the predefined outcome in a secondtime span. In some examples, and as discussed above, the third scorecorresponds to predicting the occurrence of the predefined outcome giventhe predetermined event has not occurred such as predicting a street orbridge being uncongested in the next hour given no football game occursin the next day, predicting a number of business trips for a company inthe next 3 months given the company shares value does not raise tenpercent in the next year, predicting a number of steps taken by anindividual in the next day given the individual does not gain a tenpercent weight in a next month, predicting a number of online purchasesmade by an individual in the next month given the individual does notreceive a ten percent raise in a next year, predicting the approvalrating of a politician or a TV show in the next month given a the misuseof funds by the TV station or the politician is not reported in the nextyear, predicting making a payment by an entity in the next month giventhe entity does not go delinquent in the next 3 months, and/or the like.

At step 610, the method 600 may include classifying the entity based onthe final score and adjusting the services based on the classificationand/or the final score. In some examples, classifying the entity can beperformed by the classifier module 112, the classifier module 222, theclassifier module 262, the classifier module 328, and/or the like. Asdiscussed above, based on the final score and/or the classification theservice provided to the entity, the services provided by the entity, oractions performed by the entity can be adjusted. In some examples, thenumber of lanes of the street or bridge is adjusted to preventcongestion, the number of business trips is reduced by giving anincentive to the employees, the number of steps taken by the individualis increased by giving an incentive to the individual, the number ofonline purchases made by the individual is increased by sending couponsto the individual, the services provided to the entity are decreases sothe entity makes on time payments, and/or the like.

The processes of method 600 are now described in the context ofrepresentative example related to determining whether an entity willmake a payment during a next payment cycle. In some examples, at step602, data related to an account of an entity that is in delinquency andis more than two payment cycles delinquent is collected. A short-termtime span equal to one month (i.e., the payment cycle) and a long-termtime span equal to three months are selected. At step 604, a long-term(e.g., 3 month) behavior prediction model of the entity is created basedon the collected data. Also, at step 604, a predetermined event isselected. The predetermined event is selected as the event that the(account of the) entity misses more than two cycles of payment in thenext three payment cycles, i.e., the entity account would become morethan 2+ payments delinquent in the next three payment cycles. Thelong-term behavior model predicts whether the entity account wouldbecome more than 2+ payments delinquent in the next three paymentcycles.

In some examples, at step 606, two short-term (e.g., 1 month) behaviorprediction models of the entity are created based on the collected data.Also, at step 606, an outcome is defined as that “the account would makea payment in the next payment cycle”. A first short-term behaviorprediction model is created. The first short-term behavior predictionmodel is created based on that the predetermined event happens. Thus,the first short-term behavior prediction model predicts whether theentity account would make a payment in the next payment cycle given theentity account would become more than 2+ payments delinquent in the nextthree payment cycles (long-term). Additionally, at step 606, a secondshort-term behavior prediction model is created. Contrary to the firstshort-term behavior prediction model, the second short-term behaviorprediction model is created based on that the predetermined event doesnot happen. Thus, the second short-term behavior prediction modelpredicts whether the entity account would make a payment in the nextpayment cycle given the entity account would not become more than 2+payments delinquent in the next three payment cycles.

In some examples, at step 608, the first score, the second score, andthe third score are generated. The first score is generated based on thelong-term behavior prediction model and corresponds to a prediction thatthe predetermined event of the entity account become more than 2+payments delinquent in the next three payment (long term) occurs. Thesecond score is generated based on the short-term behavior predictionmodel and corresponds to a prediction that the account would make apayment in the next payment cycle (short-term), given it would becomemore than 2+ payments delinquent in the next three payment cycles(long-term). The third score is generated based on the short-termbehavior prediction model and corresponds to a prediction that theaccount would make a payment in the next payment cycle (short-term),given it would deteriorate to one cycle delinquent or become current inthe next three payment cycles (long-term). The second score and thethird score are based on two complementary events of i) being more than2+ payments delinquent, and 2) being one cycle delinquent or becomingcurrent. The final score is determined based on the first score, thesecond score, and the third score as: final score=second score*firstscore+third score*(1−first score). In some examples, the final scorecorresponds to a prediction of the outcome in the short-term, e.g.,making a payment in the next payment cycle. In some examples, the scoresare probabilities and the final score is a probability of making apayment in the next payment cycle.

The present disclosure, the accompanying figures, and the claims are notintended to limit the present disclosure to the example embodimentsdisclosed. As such, it is contemplated that various alternateembodiments and/or modifications to the embodiment disclosed, whetherexplicitly described or implied herein, are possible in light of thedisclosure and/or the figures. Having thus described embodiments of thepresent disclosure, persons of ordinary skill in the art will recognizethat changes may be made to the embodiments disclosed without departingfrom the scope of the present disclosure.

1. A system for providing one or more services to an entity, comprising:a non-transitory memory storing at least one database; and one or morehardware processors configured to execute instructions to cause thesystem to perform operations comprising: collecting, by a datacollection and processing module of a data engine, from the database,data corresponding to a performance of the entity; generating, by amodeling module of the data engine, a first behavior model associatedwith the performance of the entity in a first time span based at leaston the collected data; determining whether a predetermined eventoccurred in the first time span; generating, by the modeling module andbased at least on the collected data, a second behavior model and athird behavior model associated with the performance of the entity in asecond time span, wherein the second behavior model is further based onthe occurrence of the predetermined event and the third behavior modelis further based on a non-occurrence of the predetermined event;determining a predefined outcome in the second time span; generating, bya score generating module of the data engine, a first score based atleast on the first behavior model and corresponding to the occurrence ofthe predetermined event, a second score based at least on the secondbehavior model, and a third score based at least on the third behaviormodel, wherein the second and third scores correspond to a likelihoodthat the predefined outcome occurs; generating, by the score generatingmodule, and based at least on the first score, the second score, and thethird score, a final score that corresponds to achieving the predefinedoutcome corresponding to the performance of the entity in a final timespan, the final time span having a duration greater than or equal to aminimum of a duration of the first time span and a duration of thesecond time span; classifying the entity into two or more groups basedat least on the final score by a classifier module of the data engine;and automatically adjusting at least one of the one or more servicesprovided to the entity based on the final score, the classification, orboth.
 2. The system of claim 1, wherein the modeling module isconfigured to implement Bayesian methodology, Gradient Boostingtechniques, or both to generate the first, the second, and the thirdbehavior models.
 3. The system of claim 1, wherein at least the firstscore is generated on a probability scale between zero and one, and thefinal score is calculated as second score*first score+thirdscore*(1−first score).
 4. The system of claim 1, wherein the first scorehas a first accuracy, the second score has a second accuracy, the thirdscore has a third accuracy, and the final score has a final predictionaccuracy better than the first accuracy, the second accuracy, and thethird accuracy; and wherein the operations further comprise:determining, by the score generating module and based at least on thefirst behavior model, the second behavior model, and the third behaviormodel, the first accuracy, the second accuracy, the third accuracy, andthe final prediction accuracy.
 5. The system of claim 1, whereincollecting data by the data collection and processing module furthercomprises normalizing data and removing one or more outliers.
 6. Thesystem of claim 1, wherein generating the second behavior model and thethird behavior model further comprises determining independentrelationships in the collected data in different spans of time equal tothe first time span and the second time span.
 7. The system of claim 1,wherein the operations performed further comprise: receiving updateddata for the entity by an incremental data module of the data engine;monitoring the received updated data; automatically updating the firstscore, the second score, and the third score based at least on thereceived updated data; automatically generating an updated final scorebased at least on the updated first score, the second score, and thethird score; and automatically reclassifying the entity based at leaston the updated final score.
 8. The system of claim 1, wherein theclassification of the entity further comprises: assigning the entity toa success group if the final score is greater than a first predeterminedthreshold, assigning the entity to a failure group if the final score isless than a second predetermined threshold, and assigning the entity toa neutral group if the final score is between the first and secondpredetermined thresholds; and wherein adjusting the services furthercomprises sending notifications or stopping at least one of the one ormore services when the entity is assigned to the failure group.
 9. Anon-transitory machine-readable medium having stored thereonmachine-readable instructions executable to cause a machine to performoperations comprising: providing one or more services to an entity;collecting, by a data collection and processing module of a data engine,from a database, data corresponding to a performance of the entity;generating, by a modeling module of the data engine, a first behaviormodel associated with the performance of the entity in a first time spanbased at least on the collected data; determining whether apredetermined event occurred in the first time span; generating, by themodeling module and based at least on the collected data, a secondbehavior model and a third behavior model associated with theperformance of the entity in a second time span, wherein the secondbehavior model is further based on the occurrence of the predeterminedevent and the third behavior model is further based on a non-occurrenceof the predetermined event; determining a predefined outcome in thesecond time span; generating, by a score generating module of the dataengine, a first score based at least on the first behavior model andcorresponding to the occurrence of the predetermined event, a secondscore based at least on the second behavior model, and a third scorebased at least on the third behavior model, wherein the second and thirdscores correspond to a likelihood that the predefined outcome occurs;and generating, by the score generating module, and based at least onthe first score, the second score, and the third score, a final scorethat corresponds to achieving the predefined outcome corresponding tothe performance of the entity in a final time span, the final time spanhaving a duration greater than or equal to a minimum of a duration ofthe first time span and a duration of the second time span.
 10. Thenon-transitory machine-readable medium of claim 9, where in theoperations further comprises: classifying the entity into two or moregroups based at least on the final score by a classifier module of thedata engine; and automatically adjusting at least one of the one or moreservices provided to the entity based on the final score, theclassification, or both.
 11. The non-transitory machine-readable mediumof claim 10, wherein the first score has a first accuracy, the secondscore has a second accuracy, the third score has a third accuracy, andthe final score has a final prediction accuracy better than the firstaccuracy, the second accuracy, and the third accuracy.
 12. Thenon-transitory machine-readable medium of claim 11, the operationsfurther comprise: determining, by the score generating module and basedat least on the first behavior model, the second behavior model, and thethird behavior model, the first accuracy, the second accuracy, the thirdaccuracy, and the final prediction accuracy.
 13. The non-transitorymachine-readable medium of claim 10, wherein generating the secondbehavior model and the third behavior model further comprisesdetermining relationships in the collected data in different spans oftime equal to the first time span and the second time span.
 14. Thenon-transitory machine-readable medium of claim 10, wherein theoperations further comprise: receiving updated data for the entity by anincremental data module of the data engine; monitoring the receivedupdated data; automatically updating the first score, the second score,and the third score based on the received updated data; automaticallygenerating an updated final score based at least on the updated firstscore, the second score, and the third score; and automaticallyreclassifying the entity based at least on the updated final score. 15.A method of providing one or more services to an entity, comprising:collecting, by a data engine executing on a server, from a database,data corresponding to a performance of the entity; generating a firstbehavior model associated with the performance of the entity in a firsttime span, wherein the collected data is used by the data engine togenerate the first behavior model; determining whether a predeterminedevent occurred in the first time span; generating a second behaviormodel and a third behavior model associated with the performance of theentity in a second time span using the collected data, wherein thesecond behavior model is based at least on the occurrence of thepredetermined event and the third behavior model is based at least on anon-occurrence of the predetermined event; determining a predefinedoutcome in the second time span; generating, by the data engine, a firstscore based at least on the first behavior model and corresponding tothe occurrence of the predetermined event, a second score based at leaston the second behavior model, and a third score based at least on thethird behavior model, wherein the second and third scores correspond toa likelihood that the predefined outcome occurs; generating, based atleast on the first score, the second score, and the third score, a finalscore that corresponds to achieving the predefined outcome correspondingto the performance of the entity in a final time span, the final timespan having a duration greater than or equal to a minimum of a durationof the first time span and a duration of the second time span;classifying the entity into two or more groups based at least on thefinal score by the data engine; and adjusting at least one of the one ormore services provided to the entity based on the final score, theclassification, or both.
 16. The method of claim 15, further comprising:generating the first, the second, and the third behavior models based onBayesian methodology, Gradient Boosting techniques, or both.
 17. Themethod of claim 15, further comprising: generating, at least the firstscore, on a probability scale between zero and one; and calculating thefinal score as second score*first score+third score*(1−first score). 18.The method of claim 15, further comprising: receiving updated data forthe entity by the data engine; monitoring the received updated data;automatically updating the first score, the second score, and the thirdscore based on the received updated data; automatically generating anupdated final score based at least on the updated first score, thesecond score, and the third score; and automatically reclassifying theentity based at least on the updated final score.
 19. The method ofclaim 15, wherein the first score has a first accuracy, the second scoreis has a second accuracy, the third score has a third accuracy, and thefinal score has a final prediction accuracy better than the firstaccuracy, the second accuracy, and the third accuracy; and wherein themethod further comprises: determining, by the data engine and based atleast on the first behavior model, the second behavior model, and thethird behavior model, the first accuracy, the second accuracy, the thirdaccuracy, and the final prediction accuracy.
 20. The method of claim 15,wherein collecting data corresponding to the performance of the entitycomprises normalizing data and removing one or more outliers